With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful--possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?

    I loved the tutorial-style game-like program development. This book rekindled my earliest joys of programming. Plus, JavaScript!" —Brendan Eich, creator of JavaScriptJavaScript is the language of the Web, and it's at the heart of every modern website from the lowliest personal blog to the mighty Google Apps. Though it's simple for beginners to pick up and play with, JavaScript is not a toy—it's a flexible and complex language, capable of much more than the showy tricks most programmers use it for.Eloquent JavaScript goes beyond the cut-and-paste scripts of the recipe books and teaches you to write code that's elegant and effective. You'll start with the basics of programming, and learn to use variables, control structures, functions, and data structures. Then you'll dive into the real JavaScript artistry: higher-order functions, closures, and object-oriented programming.Along the way you'll learn to:Master basic programming techniques and best practices Harness the power of functional and object-oriented programming Use regular expressions to quickly parse and manipulate strings Gracefully deal with errors and browser incompatibilities Handle browser events and alter the DOM structure Most importantly, Eloquent JavaScript will teach you to express yourself in code with precision and beauty. After all, great programming is an art, not a science—so why settle for a killer app when you can create a masterpiece?

    And I mean all the time. Every day there’s a new cloud-based dating app; a new cloud-based gizmo for your house; a new cloud-based game; or a thousand other new things—all in the cloud.The cloud is everywhere! Everything is in the cloud! What does it mean! Let’s slow down. Take a deep breath. That’s good. Take another. Excellent. This book teaches you all about the cloud. I’ll let you in on a little secret: the cloud is not that hard to understand. It’s not. It’s just that nobody has taken the time to explain to you what the cloud is. They haven’t, have they?Deep down I think this is because they don’t understand the cloud either, but I do. I’ve been a programmer and writer for over 30 years. I’ve been in cloud computing since the very start, and I’m here to help you on your journey to understand the cloud. Consider me your tour guide. I’ll be with you every step of the way, but not in a creepy way.I take my time with this book. I go slow and easy, so you can build up an intuition about what the cloud really is, one idea at a time. When you finish reading, you’ll understand the cloud. When you hear someone say some new cool thing is in the cloud, you’ll understand exactly what they mean. That’s a promise. How do I deliver on that promise? I use lots and lots of pictures. I use lots and lots of examples. We’ll reveal the secret inner-workings of AWS, Netflix, Facebook Messenger, Amazon Kindle, Apple iCloud, Google Maps, Nest and cloud DVRs. You’ll learn by seeing and understanding; no matter if you're a complete beginner, someone who knows a little and wants to learn more, or a programmer looking to change their career to the cloud.The cloud is the future. You don't want to miss out on the future, do you? Read this book and we'll discover it together.I’m excited. This will be fun. Let’s get started!

    Experienced .NET and ASP.NET developers will find all the important information they need to build dynamic, data-driven websites with ASP.NET and the newest release of Microsoft's Model-View-Controller technology. Featuring step-by-step guidance and lots of code samples, this guide gets you started and moves all the way to advanced topics, using plenty of examples.Designed to give experienced .NET and ASP.NET programmers everything needed to work with the newest version of MVC technology Expert author team includes Microsoft ASP.NET MVC insiders as well as leaders of the programming community Covers controllers, views, models, forms and HTML helpers, data annotation and validation, membership, authorization, security, and routing Includes essential topics such as Ajax and jQuery, NuGet, dependency injection, unit testing, extending MVC, and Razor Includes additional real-world coverage requested by readers of the previous edition as well as a new case study example chapter

    How can software developers, programmers and managers meet the challenges of the 90s and begin to resolve the software crisis?

     Coauthored by the designer of the Scala language, this authoritative book will teach you, one step at a time, the Scala language and the ideas behind it. The book is carefully crafted to help you learn. The first few chapters will give you enough of the basics that you can already start using Scala for simple tasks. The entire book is organized so that each new concept builds on concepts that came before - a series of steps that promises to help you master the Scala language and the important ideas about programming that Scala embodies. A comprehensive tutorial and reference for Scala, this book covers the entire language and important libraries.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES - Exploring R, RStudio, and R packages - Using R for math: variable types, vectors, calling functions, and more - Exploiting data structures, including data.frames, matrices, and lists - Creating attractive, intuitive statistical graphics - Writing user-defined functions - Controlling program flow with if, ifelse, and complex checks - Improving program efficiency with group manipulations - Combining and reshaping multiple datasets - Manipulating strings using R's facilities and regular expressions - Creating normal, binomial, and Poisson probability distributions - Programming basic statistics: mean, standard deviation, and t-tests - Building linear, generalized linear, and nonlinear models - Assessing the quality of models and variable selection - Preventing overfitting, using the Elastic Net and Bayesian methods - Analyzing univariate and multivariate time series data - Grouping data via K-means and hierarchical clustering - Preparing reports, slideshows, and web pages with knitr - Building reusable R packages with devtools and Rcpp - Getting involved with the R global community

    Success key books for: Programming puzzles for interviews Campus Preparation Degree/Masters Course Preparation Instructor's GATE Preparation Big job hunters: Microsoft, Google, Amazon, Yahoo, Flip Kart, Adobe, IBM Labs, Citrix, Mentor Graphics, NetApp, Oracle, Webaroo, De-Shaw, Success Factors, Face book, McAfee and many more Reference Manual for working people

    In this new first edition, well-known author Behrouz Forouzan uses his accessible writing style and visual approach to simplify the difficult concepts of cryptography and network security. This edition also provides a website that includes Powerpoint files as well as instructor and students solutions manuals. Forouzan presents difficult security topics from the ground up. A gentle introduction to the fundamentals of number theory is provided in the opening chapters, paving the way for the student to move on to more complex security and cryptography topics. Difficult math concepts are organized in appendices at the end of each chapter so that students can first learn the principles, then apply the technical background. Hundreds of examples, as well as fully coded programs, round out a practical, hands-on approach which encourages students to test the material they are learning.

    Each idiom comes with a detailed description, example code showing the "wrong" way to do it, and code for the idiomatic, "Pythonic" alternative. *This version of the book is for Python 2.7.3+. There is also a Python 3.3+ version available.* "Writing Idiomatic Python" contains the most common and important Python idioms in a format that maximizes identification and understanding. Each idiom is presented as a recommendation to write some commonly used piece of code. It is followed by an explanation of why the idiom is important. It also contains two code samples: the "Harmful" way to write it and the "Idiomatic" way. * The "Harmful" way helps you identify the idiom in your own code. * The "Idiomatic" way shows you how to easily translate that code into idiomatic Python. This book is perfect for you: * If you're coming to Python from another programming language * If you're learning Python as a first programming language * If you're looking to increase the readability, maintainability, and correctness of your Python code What is "Idiomatic" Python? Every programming language has its own idioms. Programming language idioms are nothing more than the generally accepted way of writing a certain piece of code. Consistently writing idiomatic code has a number of important benefits: * Others can read and understand your code easily * Others can maintain and enhance your code with minimal effort * Your code will contain fewer bugs * Your code will teach others to write correct code without any effort on your part

    Since that time, this text has been revised, tweaked, and enhanced to account for the changes found within each release of the .NET platform (1.1, 2.0, 3.0 and now 3.5)..NET 3.0 was more of an augmentative release, essentially providing three new APIs: Windows Presentation Foundation (WPF), Windows Communication Foundation (WCF) and Windows Workflow Foundation (WF). As you would expect, coverage of the "W's" has been expanded a great deal in this version of the book from the previous Special Edition text.Unlike .NET 3.0, .NET 3.5 provides dozens of C# language features and .NET APIs. This edition of the book will walk you through all of this material using the same readable approach as was found in previous editions. Rest assured, you'll find detailed coverage of Language Integrated Query (LINQ), the C# 2008 language changes (automatic properties, extension methods, anonymous types, etc.) and the numerous bells and whistles of Visual Studio 2008. What you'll learn Everything you need to knowget up to speed with C# 2008 quickly and efficiently. Discover all the new .NET 3.5 featuresLanguage Integrated Query, anonymous types, extension methods, automatic properties, and more. Get a professional footholdtargeted to appeal to experienced software professionals, this book gives you the facts you need the way you need to see them. A rock-solid foundationfocuses on everything you need to be a successful .NET 3.5 programmer, not just the new features. Get comfortable with all the core aspects of the platform including assemblies, remoting, Windows Forms, Web Forms, ADO.NET, XML web services, and much more. Who this book is forIf you're checking out this book for the first time, understand that it targets experienced software professionals and/or students of computer science (so please don't expect three chapters devoted to "for" loops). The mission of this text is to provide you with a rock-solid foundation to the C# 2008 programming language and the core aspects of the .NET platform (object-oriented programming, assemblies, file IO, Windows Forms/WPF, ASP.NET, ADO.NET, WCF, WF, etc.). Once you digest the information presented in these 33 chapters, you'll be in a perfect position to apply this knowledge to your specific programming assignments, and you'll be well equipped to explore the .NET universe on your own terms. "